Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/psu-efd/pyHMT2DWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/rules/psu-efd/pyhmt2d/calibration-parametric)<a href="https://agentmods.dev/rules/psu-efd/pyhmt2d/calibration-parametric"><img src="https://agentmods.dev/badge/rules/psu-efd/pyhmt2d/calibration-parametric/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/rules/psu-efd/pyhmt2d/calibration-parametric"><img src="https://agentmods.dev/badge/rules/psu-efd/pyhmt2d/calibration-parametric.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.00536 |
| Opus 5.5 | $0.00000 | $0.00214 |
| Sonnet 5.5 | $0.00000 | $0.00107 |
| Haiku 4.5 | $0.00000 | $0.00054 |
Grade A, and why
calibration-parametric scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Calibration and Parametric Study
This rule covers the calibration and parametric study capabilities of pyHMT2D.
Model Calibration
Location: pyHMT2D/Calibration/
Features
- Automatic calibration using scipy's optimize module
- Support for both local and global optimization methods
- Configurable through JSON configuration files
- Support for multiple calibration parameters
- Objective function customization
Usage Example
import pyHMT2D
# Create calibrator instance
my_calibrator = pyHMT2D.Calibration.Calibrator("calibration.json")
# Run calibration
my_calibrator.calibrate()
Configuration File Format
{
"model_type": "SRH-2D",
"parameters": {
"manning_n": {
"initial": 0.03,
"min": 0.01,
"max": 0.1
}
},
"objective_function": {
"type": "rmse",
"observed_data": "path/to/observed.csv"
}
}
Parametric Study
Location: pyHMT2D/Parametric_Study/
Features
- Systematic parameter variation
- Batch simulation management
- Result collection and analysis
- Support for multiple parameters
- Parallel execution support
Usage Example
import pyHMT2D
# Create parametric study instance
my_study = pyHMT2D.Parametric_Study.ParametricStudy("study_config.json")
# Run study
my_study.run()
Study Types
-
Single Parameter Studies
- Vary one parameter while keeping others constant
- Useful for sensitivity analysis
-
Multi-Parameter Studies
- Full factorial designs
- Latin Hypercube sampling
- Custom parameter combinations
-
Monte Carlo Studies
- Random parameter sampling
- Statistical analysis of results
- Uncertainty quantification
Result Analysis
Common analysis tools for both calibration and parametric studies:
-
Statistical Analysis
- Mean, standard deviation
- Confidence intervals
- Sensitivity indices
-
Visualization
- Parameter-response plots
- Contour plots
- Time series analysis
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 106 lines · 0 tokens per session scan A 497ecfbe7193
calibration-parametric is a cursor rule published in the GitHub repository psu-efd/pyHMT2D (133 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 536 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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